Highlights
This role combines hands-on technical leadership with product thinking, partnering across business, product, data, and engineering teams to deliver copilots, agentic AI systems, RAG solutions, intelligent automation, and reusable AI frameworks that drive measurable business impact. You will lead the development of enterprise-grade AI applications, agentic systems, copilots, retrieval-augmented generation (RAG) solutions, and intelligent workflow automation that transform how teams discover insights, make decisions, and deliver value.
Numbers & Facts
| Location | Dearborn, MI |
Description
As the AI Engineering Lead, you will architect, build, and scale AI-powered solutions that accelerate both the delivery of Integrated Services Data, AI & Analytics (ISDAIA) products and the adoption of AI capabilities across the Integrated Services business.
You will lead the development of enterprise-grade AI applications, agentic systems, copilots, retrieval-augmented generation (RAG) solutions, and intelligent workflow automation that transform how teams discover insights, make decisions, and deliver value.
This role combines hands-on technical leadership with product thinking and strategic execution. You will partner closely with Product Managers, Engineering Teams, Analytics Leaders, and Business Stakeholders to identify high-value use cases, develop reusable AI capabilities, and enable responsible AI adoption at scale.
You will play a key role in shaping the future AI ecosystem for Integrated Services by building scalable frameworks, shared services, and AI-enabled experiences that improve business outcomes, operational efficiency, and speed to delivery.
The AI Engineering Lead will architect, build, and scale enterprise AI solutions that accelerate analytics delivery, improve decision-making, and enable responsible AI adoption across Integrated Services. This role combines hands-on technical leadership with product thinking, partnering across business, product, data, and engineering teams to deliver copilots, agentic AI systems, RAG solutions, intelligent automation, and reusable AI frameworks that drive measurable business impact.
Strategic Thinking & Leadership
Partner with business leaders and product teams to identify high-value AI opportunities and translate them into scalable AI-powered solutions.
Define and communicate AI solution vision, roadmaps, and measurable success metrics.
Drive AI strategy across Generative AI, Agentic AI, conversational experiences, AI-enabled analytics, and intelligent automation initiatives.
Establish governance frameworks for Responsible AI, security, compliance, scalability, and enterprise adoption.
Lead cross-functional AI programs and influence executive stakeholders through compelling business cases, demonstrations, and measurable outcomes.
Technical Leadership & Expertise
Architect and oversee end-to-end AI solutions, including:
Conversational AI and Copilot experiences
Retrieval-Augmented Generation (RAG) architectures
Agentic AI frameworks and multi-agent orchestration systems
AI-powered analytics and insight generation solutions
Natural language interfaces for analytics and business intelligence
Intelligent workflow automation and decision-support capabilities
Semantic search and enterprise knowledge management solutions
Strong proficiency in Google Cloud Platform (GCP) services for AI development (Vertex AI, BigQuery, Cloud Storage, Dataflow).
Experience designing and deploying enterprise AI solutions leveraging Large Language Models (LLMs), foundation models, prompt engineering, and model evaluation frameworks.
Experience building AI systems using Python-based ecosystems and modern AI frameworks.
Experience with vector databases, embeddings, semantic search, grounding techniques, and retrieval architectures.
Implement scalable AI Engineering, MLOps, and LLMOps practices including CI/CD, prompt versioning, testing, governance, monitoring, and lifecycle management.
Proficiency in Git, Docker, API-based deployments, cloud-native architectures, and scalable AI services.
Apply strong software engineering practices including modular design, testing, observability, security, and documentation.
Establish reusable AI frameworks, accelerators, and engineering patterns that improve speed, consistency, and quality of delivery.
Evaluate emerging AI technologies and identify opportunities to accelerate analytics delivery and business adoption.
Support architectural reviews and ensure best practices across AI systems, platforms, and products.
Implement Responsible AI principles including governance, explainability, privacy, security, and ethical AI compliance.
Delivery Focus
Own end-to-end AI solution delivery in partnership with Product, Engineering, Data, and Business teams.
Ensure production-grade deployment of AI applications, copilots, and agent-based solutions using containerization, orchestration, and scalable cloud infrastructure.
Build reusable AI accelerators, frameworks, and services that improve speed-to-delivery across the ISDAIA portfolio.
Partner with product teams to embed AI capabilities directly into dashboards, self-service analytics platforms, applications, and business workflows.
Influence investment decisions using measurable business impact, adoption metrics, operational efficiencies, and ROI analysis.
Establish monitoring frameworks for AI performance, solution effectiveness, reliability, governance, and user adoption.
Team Development & Community Leadership
Lead and mentor AI engineers while establishing best practices for enterprise AI development.
Build AI engineering standards, reusable frameworks, shared tooling, libraries, and delivery patterns across ISDAIA.
Promote knowledge sharing through Communities of Practice and AI Centers of Excellence.
Foster a culture of experimentation, continuous learning, innovation, and engineering excellence.
Support talent development in emerging AI disciplines including Generative AI, Agentic AI, conversational experiences, and intelligent automation.
Serve as a thought leader for enterprise AI adoption and AI-enabled transformation initiatives.
Strategic Thinking & Leadership
Partner with business leaders and product teams to identify high-value AI opportunities and translate them into scalable AI-powered solutions.
Define and communicate AI solution vision, roadmaps, and measurable success metrics.
Drive AI strategy across Generative AI, Agentic AI, conversational experiences, AI-enabled analytics, and intelligent automation initiatives.
Establish governance frameworks for Responsible AI, security, compliance, scalability, and enterprise adoption.
Lead cross-functional AI programs and influence executive stakeholders through compelling business cases, demonstrations, and measurable outcomes.
Technical Leadership & Expertise
Architect and oversee end-to-end AI solutions, including:
Conversational AI and Copilot experiences
Retrieval-Augmented Generation (RAG) architectures
Agentic AI frameworks and multi-agent orchestration systems
AI-powered analytics and insight generation solutions
Natural language interfaces for analytics and business intelligence
Intelligent workflow automation and decision-support capabilities
Semantic search and enterprise knowledge management solutions
Strong proficiency in Google Cloud Platform (GCP) services for AI development (Vertex AI, BigQuery, Cloud Storage, Dataflow).
Experience designing and deploying enterprise AI solutions leveraging Large Language Models (LLMs), foundation models, prompt engineering, and model evaluation frameworks.
Experience building AI systems using Python-based ecosystems and modern AI frameworks.
Experience with vector databases, embeddings, semantic search, grounding techniques, and retrieval architectures.
Implement scalable AI Engineering, MLOps, and LLMOps practices including CI/CD, prompt versioning, testing, governance, monitoring, and lifecycle management.
Proficiency in Git, Docker, API-based deployments, cloud-native architectures, and scalable AI services.
Apply strong software engineering practices including modular design, testing, observability, security, and documentation.
Establish reusable AI frameworks, accelerators, and engineering patterns that improve speed, consistency, and quality of delivery.
Evaluate emerging AI technologies and identify opportunities to accelerate analytics delivery and business adoption.
Support architectural reviews and ensure best practices across AI systems, platforms, and products.
Implement Responsible AI principles including governance, explainability, privacy, security, and ethical AI compliance.
Delivery Focus
Own end-to-end AI solution delivery in partnership with Product, Engineering, Data, and Business teams.
Ensure production-grade deployment of AI applications, copilots, and agent-based solutions using containerization, orchestration, and scalable cloud infrastructure.
Build reusable AI accelerators, frameworks, and services that improve speed-to-delivery across the ISDAIA portfolio.
Partner with product teams to embed AI capabilities directly into dashboards, self-service analytics platforms, applications, and business workflows.
Influence investment decisions using measurable business impact, adoption metrics, operational efficiencies, and ROI analysis.
Establish monitoring frameworks for AI performance, solution effectiveness, reliability, governance, and user adoption.
Team Development & Community Leadership
Lead and mentor AI engineers while establishing best practices for enterprise AI development.
Build AI engineering standards, reusable frameworks, shared tooling, libraries, and delivery patterns across ISDAIA.
Promote knowledge sharing through Communities of Practice and AI Centers of Excellence.
Foster a culture of experimentation, continuous learning, innovation, and engineering excellence.
Support talent development in emerging AI disciplines including Generative AI, Agentic AI, conversational experiences, and intelligent automation.
Serve as a thought leader for enterprise AI adoption and AI-enabled transformation initiatives.